Development of a deep learning toolkit for MRI-guided online adaptive radiotherapy

在德国，每年有近50万新患者被诊断出患有癌症。超过一半的患者接受放射治疗作为治疗的一部分。室内成像与现代外部放射治疗技术相结合，可以严格调整对目标的递送剂量。然而，这些技术的全部潜力目前尚未得到充分利用。其中一个原因是存在解剖学上的变化。对于目前绝大多数放疗患者来说，在治疗计划中通过在肿瘤体积周围引入安全边际来考虑解剖改变，这增加了照射体积，对危险器官（OARs）的剂量负担，并最终限制了适用剂量。通过在线适应性放射治疗（ART）可以实现改进的治疗：不是在整个治疗过程中应用相同的照射计划，而是根据室内成像推断的治疗位置的日常解剖，在每个照射阶段优化治疗。为了实现磁共振成像（MRI）作为室内成像方式的整合，人们付出了巨大的努力，但直到最近几年，集成的MRI -linacs才在少数学术机构得到临床应用。优越的软组织对比度允许目标和桨的精确可视化，这使得基本的在线ART工作流程得以实施。从MRI生成合成ct和为优化治疗计划而重新勾画患者的时间开销极大地限制了对所有患者的适用性。该项目的目标是解决在线计划适应的主要瓶颈：合成CT生成和器官描绘。通过结合我们各自在放射治疗和人工智能方面的专业知识，我们的目标是提出基于深度学习的解决方案，以建立一个工具包来应对这两个挑战。在合成CT生成的情况下，我们将使用具有混合成对-非成对训练范例的生成对抗网络，另外利用新的嵌入损失函数来确保几何保真度。或者，将研究解纠缠表示。对于分割，我们的目标是通过使用纵向空间记忆网络进行时空分割来利用以前分数的信息。最后，我们的目标是通过评估这些方法在计划优化方面的临床影响来评估这些方法的好处，包括医生对描绘的分级和稳健性评估。时间效益，包括对分割和合成CT的潜在修正，也将被量化。

In Germany close to 500,000 new patients are diagnosed with cancer every year. More than half of the patients undergo radiotherapy as part of their treatment. In-room imaging, in combination with modern external beam radiotherapy techniques, enables tight adjustment of the delivered dose to the target. However, the full potential of these techniques is currently not exploited. One of the reasons for this is the presence of anatomical changes. For the vast majority of patients in today’s radiotherapy, anatomical alterations are considered by introducing safety margins around the tumor volume during treatment planning, which increases the irradiated volume, the dose burden to organs at risk (OARs), and eventually limits the applicable dose.An improved treatment can be realized by online adaptive radiation therapy (ART): instead of applying the same irradiation plan throughout the entire course of treatment, the treatment is optimized at each irradiation session on basis of the daily anatomy in treatment position, as inferred from in-room imaging. Great efforts have been made to realize the integration of magnetic resonance imaging (MRI) as in-room imaging modality, but only during the last few years integrated MR-linacs became clinically available at few academic institutions. The superior soft-tissue contrast allows for accurate visualization of targets and OARs, which has allowed basic online ART workflows to be implemented. The time overhead associated with the generation of a synthetic-CT from MRI and a re-delineation of the patient for treatment plan optimization greatly limits applicability to all patients.The goal of this project is to tackle the main bottlenecks of online plan adaptation: synthetic CT generation and organ delineation. By combining our respective expertise in radiotherapy and artificial intelligence, we aim at proposing deep-learning-based solutions to build a toolkit to tackle both of these challenges. In the case of synthetic CT generation, we will use generative adversarial networks with hybrid paired-unpaired training paradigms, additionally exploiting novel embedding loss functions to ensure geometric fidelity. Alternatively, disentangled representations will be investigated. For segmentation we aim at exploiting the information from previous fractions by performing spatio-temporal segmentation using longitudinal spatial memory networks. Finally, we aim at evaluating the benefits from these approaches by performing an evaluation of their clinical impact in terms of plan optimization, including physician grading of delineations and robustness assessment. The time benefits, including potential corrections to segmentations and synthetic CT, will also be quantified.